U.S. patent number 10,642,247 [Application Number 15/685,129] was granted by the patent office on 2020-05-05 for cell control system.
This patent grant is currently assigned to FANUC CORPORATION. The grantee listed for this patent is FANUC CORPORATION. Invention is credited to Manabu Saitou.
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United States Patent |
10,642,247 |
Saitou |
May 5, 2020 |
Cell control system
Abstract
A cell control system capable of estimating a cause of an alarm
by estimating an influence of noise in a plurality of machines
includes a machine operation instruction unit for transmitting an
operation instruction to a managed manufacturing machine, a noise
value collection unit for collecting detected noise information, an
operation information collection unit for collecting operation
information of a manufacturing machine, a learning unit for
creating a learning model by performing machine learning using the
collected operation information collected as an input signal and
the detected noise information as an instruction signal, an
estimation unit for analyzing the learning model to estimate
operation information corresponding to a noise factor, and an
operation instruction change unit for instructing the machine
operation instruction unit to change instruction content based on
the operation information corresponding to the noise factor.
Inventors: |
Saitou; Manabu (Yamanashi,
JP) |
Applicant: |
Name |
City |
State |
Country |
Type |
FANUC CORPORATION |
Yamanashi |
N/A |
JP |
|
|
Assignee: |
FANUC CORPORATION (Yamanashi,
JP)
|
Family
ID: |
61166667 |
Appl.
No.: |
15/685,129 |
Filed: |
August 24, 2017 |
Prior Publication Data
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Document
Identifier |
Publication Date |
|
US 20180059639 A1 |
Mar 1, 2018 |
|
Foreign Application Priority Data
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|
|
|
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Aug 25, 2016 [JP] |
|
|
2016-164932 |
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06N
20/00 (20190101); G05B 19/406 (20130101); G05B
2219/37337 (20130101); Y02P 90/02 (20151101); Y02P
90/18 (20151101); G05B 2219/36487 (20130101); G05B
2219/33296 (20130101); Y02P 90/14 (20151101); Y02P
90/20 (20151101); G06N 3/02 (20130101) |
Current International
Class: |
G05B
19/406 (20060101); G06N 20/00 (20190101); G06N
3/02 (20060101) |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
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|
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H2-272326 |
|
Nov 1990 |
|
JP |
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8-320726 |
|
Dec 1996 |
|
JP |
|
2003-308107 |
|
Oct 2003 |
|
JP |
|
2004-206063 |
|
Jul 2004 |
|
JP |
|
2010-287227 |
|
Dec 2010 |
|
JP |
|
2011-243118 |
|
Dec 2011 |
|
JP |
|
Other References
Office Action in JP Application No. 2016-164932, dated Sep. 18,
2018, 4pp. cited by applicant .
Office Action in JP Application No. 2016-164932, dated May 7, 2019,
3pp. cited by applicant.
|
Primary Examiner: Lee; Thomas C
Assistant Examiner: Tang; Michael
Attorney, Agent or Firm: Hauptman Ham, LLP
Claims
The invention claimed is:
1. A cell control system, comprising: at least one manufacturing
cell including a first manufacturing machine and a second
manufacturing machine; and a cell controller for transmitting an
operation instruction to the first manufacturing machine and the
second manufacturing machine based on a manufacturing plan received
from a production planning device, wherein the cell controller
includes a machine operation instruction unit for transmitting the
operation instruction to the first manufacturing machine and the
second manufacturing machine based on the manufacturing plan, a
noise value collection unit for collecting a first noise value
related to noise detected by the first manufacturing machine and a
second noise value related to noise detected by the second
manufacturing machine, an operation information collection unit for
collecting first operation information of the first manufacturing
machine and second operation information of the second
manufacturing machine, a learning unit for creating a learning
model by performing machine learning using (i) the first operation
information and the second operation information collected by the
operation information collection unit as an input signal and (ii)
the first noise value collected by the noise value collection unit
as an instruction signal, and performing machine learning using (a)
the first operation information and the second operation
information collected by the operation information collection unit
as an input signal and (b) the second noise value collected by the
noise value collection unit as an instruction signal, an estimation
unit for analyzing the learning model to estimate operation
information corresponding to a cause of the noise detected by the
first manufacturing machine and operation information corresponding
to a cause of the noise detected by the second manufacturing
machine, the estimation unit being configured to estimate a trend
in a change of the noise value due to a change of the operation
information, and an operation instruction change unit for
instructing the machine operation instruction unit to change
instruction content of the operation instruction sent to at least
one of the first manufacturing machine or the second manufacturing
machine, based on the operation information corresponding to the
cause of the noise estimated by the estimation unit.
2. The cell control system according to claim 1, wherein the
estimation unit is configured to perform a prediction using the
learning model created by the learning unit to estimate a
manufacturing machine having a low noise immunity between the first
manufacturing machine and the second manufacturing machine, and the
operation instruction change unit is configured to instruct the
machine operation instruction unit to change the instruction
content of the operation instruction based on information about the
manufacturing machine having the low noise immunity estimated by
the estimation unit.
3. The cell control system according to claim 2, wherein the
operation instruction change unit is configured to instruct the
machine operation instruction unit to change the instruction
content of the operation instruction estimated to affect the
manufacturing machine having the low noise immunity estimated by
the estimation unit.
4. A cell control system, comprising: at least one manufacturing
cell including at least one manufacturing machine; and a cell
controller for transmitting an operation instruction to the
manufacturing machine based on a manufacturing plan received from a
production planning device, wherein the cell controller includes: a
machine operation instruction unit for transmitting the operation
instruction to the manufacturing machine based on the manufacturing
plan, a noise value collection unit for collecting a noise value
related to noise detected by the manufacturing machine, an
operation information collection unit for collecting operation
information of the manufacturing machine, a learning unit for
creating a learning model by performing machine learning using the
operation information collected by the operation information
collection unit as an input signal and the noise value collected by
the noise value collection unit as an instruction signal, an
estimation unit for analyzing the learning model created by the
learning unit to estimate operation information corresponding to a
cause of the noise detected by the manufacturing machine, the
estimation unit being configured to estimate a trend in a change of
the noise value due to a change of the operation information, and
an operation instruction change unit for instructing the machine
operation instruction unit to change instruction content of the
operation instruction, based on the operation information
corresponding to the cause of the noise estimated by the estimation
unit, the at least one manufacturing machine includes a plurality
of manufacturing machines, and the estimation unit is configured to
estimate a manufacturing machine having a low noise immunity among
the plurality of manufacturing machines by: selecting one piece of
the operation information collected by the operation information
collection unit, inputting the selected piece of the operation
information as an input value to the learning model, calculating a
slope of the input value at which each output value increases,
varying each input value in a direction of the calculated slope,
the varying comprising increasing or decreasing, repeating the
inputting, the calculating and the varying until one of output
values exceeds a predetermined threshold value, and estimating the
manufacturing machine corresponding to the output value exceeding
the predetermined threshold value as the manufacturing machine
having the low noise immunity.
Description
RELATED APPLICATIONS
The present application claims priority of Japanese Patent
Application No. 2016-164932 filed Aug. 25,2016 , the disclosure of
which is hereby incorporated by reference herein in its
entirety.
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to a cell control system, and
particularly relates to a cell control system that estimates an
influence of noise in a plurality of machines.
2. Description of the Related Art
FIG. 7 is a diagram illustrating a cell control system
(manufacturing control system) that manages a manufacturing cell
including a plurality of manufacturing machines. The cell control
system improves an operation rate of the whole manufacturing cell
by instructing each manufacturing cell to operate based on a
manufacturing plan indicated by a production planning device while
managing usage states of the manufacturing machines or determining
a manufacturing machine to be used.
At the time of operating a manufacturing cell according to the
manufacturing plan, when an alarm occurs in a manufacturing machine
included in the operated manufacturing cell to suspend an
operation, productivity in the manufacturing cell decreases. As a
result, an operation rate of the whole manufacturing cell
decreases.
As a conventional technology for responding to the occurrence of
the alarm in the manufacturing machine, for example, Japanese
Patent Application Laid-Open No. 2011-243118 discloses a monitoring
diagnostic device that uses a plurality of parts in a monitoring
target device as an object to be monitored, collects time series
physical quantities of an attached sensor, detects a defect,
diagnoses a cause thereof, and determines presence/absence of a
causal relationship from a correlation coefficient between sensor
data items. In addition, Japanese Patent Application Laid-Open No.
8-320726 discloses a diagnosis/analysis device that interprets a
correlated pair of signals fetched from a plurality of objects to
be controlled, analyzes presence/absence of a defect through
comparison with correlation information at normal time, and outputs
a conclusion of a fault location from presence/absence information
of the defect.
In general, measures are taken against noise (electrical
disturbance or physical vibration) in the manufacturing machine to
prevent the occurrence of the alarm. Such measures are carried out
in the following procedure.
Procedure a1) A state of noise of each machine is measured.
Procedure a2) A probability that an alarm will occur is analyzed
from a result of measurement in procedure a1.
Procedure a3) A cause of the alarm is specified from an analysis
result, and measures against the noise is taken to prevent the
cause.
However, in an environment such as a factory in which a plurality
of manufacturing machines continue to operate, an influence of
noise changes in association with a combination of a plurality of
conditions such as arrangement or wiring of the manufacturing
machines, an operation pattern, etc. Thus, in many cases, it is
difficult to specify the cause of the alarm even when the above
procedures are performed. In such a state, both the noise and an
operating condition need to be simultaneously analyzed. However, in
practice, since the plurality of manufacturing machines
simultaneously operates, the operation pattern as a whole is
complex, and analysis of the cause is difficult.
On the other hand, in the technology disclosed in Japanese Patent
Application Laid-Open No. 2011-243118, since diagnosis is performed
using only one sensor in the device, it is not effective to apply
the technology to a manufacturing cell in which a plurality of
machines simultaneously operates. In addition, in the technology
disclosed in Japanese Patent Application Laid-Open No. 8-320726,
even though a defect may be diagnosed in a machine system including
a plurality of control devices, a cause of the defect may be
estimated only in a state in which the defect is registered in a
database in advance.
SUMMARY OF THE INVENTION
In this regard, an object of the invention is to provide a cell
control system capable of estimating a cause of an alarm by
estimating an influence of noise in a plurality of machines.
In the invention, a control device of a machine included in a
manufacturing cell is connected to a cell controller. Each cell
controller monitors and collects states below of each manufacturing
machine.
1. A value of noise at each measurement position (a main body of
the control device, an amplifier, a power supply, a signal line,
etc.)
2. Operation information of each machine of the manufacturing cell
(a speed, acceleration, and load of each axis, a running line
number of block)
Then, the cell controller analyzes the collected information in the
following procedure.
Procedure b1) A correlation between the noise value and the
operation information is calculated by machine learning.
Procedure b2) A state in which noise is likely to occur (=a
combination of the operation information) is estimated from the
correlation.
Procedure b3) A machine or a part thereof in which a noise level is
likely to become high is detected from the correlation.
The cell controller reports an estimated result to an operator of
the manufacturing machine or a high-order server.
The operator may take measures against the noise by concentrating a
specific part of the manufacturing machine based on the estimated
result. For this reason, it is possible to take measures to
efficiently improve an operation rate of the manufacturing cell in
a small number of processes.
Further, a cell control system according to the invention includes
at least one manufacturing cell including at least one
manufacturing machine, and a cell controller for transmitting an
operation instruction to the manufacturing machine based on a
manufacturing plan received from a production planning device, in
which the cell controller includes a machine operation instruction
unit for transmitting the operation instruction to the
manufacturing machine based on the manufacturing plan, a noise
value collection unit for collecting detected noise information, an
operation information collection unit for collecting operation
information of the manufacturing machine, a learning unit for
creating a learning model by performing machine learning using the
operation information collected by the operation information
collection unit as an input signal and the noise information
collected by the noise value collection unit as an instruction
signal, an estimation unit for analyzing the learning model created
by the learning unit to estimate operation information
corresponding to a cause of noise detected by the manufacturing
machine, and an operation instruction change unit for instructing
the machine operation instruction unit to change instruction
content based on the operation information corresponding to a noise
factor estimated by the estimation unit.
In the control system according to the invention, the estimation
unit performs a prediction using the learning model created by the
learning unit to estimate a manufacturing machine having a low
noise immunity in the manufacturing machine, and the operation
instruction change unit instructs the machine operation instruction
unit to change communication content based on information about a
noise immunity estimated by the estimation unit.
In the control system according to the invention, the operation
instruction change unit instructs the machine operation instruction
unit to change an operation instruction estimated to affect the
manufacturing machine having the low noise immunity estimated by
the estimation unit.
According to the invention, it is possible to estimate operation
information having a large correlation with noise with regard to
each manufacturing machine. It is possible to prevent occurrence of
an alarm by taking measures against noise based on an estimated
result. In this way, it is possible to improve an operation rate of
a manufacturing machine. In addition, it is possible to detect a
correlation of noise between manufacturing machines, and to detect
deterioration of a noise immunity of a manufacturing machine
through continuous monitoring.
BRIEF DESCRIPTION OF THE DRAWINGS
The above-described object and characteristic of the invention and
other objects and characteristics will be clear from description of
embodiments below with reference to accompanying drawings. In the
drawings:
FIG. 1 is a schematic block diagram of a cell control system
according to an embodiment of the invention;
FIG. 2 is a diagram illustrating an example of a learning unit
using a multi-layer neural network;
FIG. 3 is a diagram for description of a procedure of estimation of
operation information corresponding to a cause of noise by an
estimation unit;
FIG. 4 is a diagram for description of a procedure of estimation of
operation information corresponding to a cause of noise by the
estimation unit;
FIG. 5 is a diagram for description of a procedure of estimation of
operation information corresponding to a cause of noise by the
estimation unit;
FIG. 6 is a diagram for description of a procedure of estimation of
a manufacturing machine having a low noise immunity by the
estimation unit; and
FIG. 7 is a diagram illustrating a cell control system
(manufacturing control system) that manages a manufacturing cell
including a plurality of manufacturing machines.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
Hereinafter, an embodiment of the invention will be described with
reference to drawings.
FIG. 1 is a schematic block diagram of a cell control system
according to an embodiment of the invention. In FIG. 1, a dotted
arrow indicates a flow of information in a conventional technology,
and a solid arrow indicates a flow of information introduced in the
invention. A cell control system 1 of the present embodiment is
configured by connecting a production planning device 2, a cell
controller 3, and at least one manufacturing cell 4 through a
network, etc.
The production planning device 2 plans overall manufacturing work
performed in at least one manufacturing cell 4, and transmits the
planned manufacturing work as a manufacturing plan to the cell
controller 3 that manages each manufacturing cell 4.
The cell controller 3 instructs each manufacturing cell to operate
while managing a usage state of a manufacturing machine 41 included
in a managed manufacturing cell 4 or determining a manufacturing
machine 41 to be used based on the manufacturing plan indicated by
the production planning device 2. The cell controller 3 illustrated
in FIG. 1 manages the at least one manufacturing cell 4. The cell
controller 3 includes a manufacturing plan receiver 30, a machine
operation instruction unit 31, a communication unit 32, a noise
value collection unit 33, an operation information collection unit
34, a learning unit 35, an estimation unit 36, and an operation
instruction change unit 37.
The manufacturing plan receiver 30 receives the manufacturing plan
indicated by the production planning device 2, and outputs the
received manufacturing plan to the machine operation instruction
unit 31.
The machine operation instruction unit 31 transmits an operation
instruction to the at least one manufacturing machine 41 included
in the manufacturing cell 4 managed by the cell controller 3
through the communication unit 32 based on the manufacturing plan
input from the manufacturing plan receiver 30. The machine
operation instruction unit 31 has a function of creating an
operation schedule for each manufacturing machine 41 included in
the managed manufacturing cell 4 based on the manufacturing
plan.
The noise value collection unit 33 collects a value related to
noise detected by the manufacturing machine 41 included in the
managed manufacturing cell 4 from the manufacturing machine 41, and
stores the collected value in an operation information database 38
together with time information, etc. Referring to the noise value
collected by the noise value collection unit 33, in addition to a
noise value detected by a noise detector 42 included in the
manufacturing machine 41, it is possible to collect a noise value
detected by a sensor, etc. (not illustrated) provided in a factory
or outside, or it is possible to collect all noise values that may
be acquired by the cell control system 1.
The operation information collection unit 34 collects operation
information indicating an operation state of the manufacturing
machine 41, which is included in the managed manufacturing cell 4,
from the manufacturing machine 41, and stores the collected
operation information together with time information, etc. in the
operation information database 38 for each manufacturing machine
41. In addition to the information indicating the operation state
of the manufacturing machine 41, for example, the operation
information collection unit 34 may collect, as the operation
information, all types of information such as time, a power state
of the factory, etc. which may be acquired by the cell control
system 1.
The learning unit 35 performs machine learning on a relation
between the operation information collected from each manufacturing
machine and the noise value detected from each manufacturing
machine 41 based on the noise value and the operation information
stored in the operation information database 38. Further, a model
learned by the learning unit 35 is used for estimation by the
estimation unit 36 described below. Any model may be used as a
model which is used for learning performed by the learning unit 35
when the model may estimate a trend in change of a noise value due
to a change of certain operation information.
For example, it is possible to use a multi-layer neural network, a
Bayesian network, etc. described below.
As an example, for example, when a multi-layer neural network
illustrated in FIG. 2 is used as the learning unit 35, operation
information of each manufacturing machine 41 may be given as an
input signal, and a noise value of each manufacturing machine 41
may be given as an instruction signal. In an intermediate layer 1,
operation information of the same manufacturing machine in
operation information of each manufacturing machine 41 may be
mutually learned. In a whole bonding layer, a correlation between
operation information of each manufacturing machine 41 and noise of
each manufacturing machine 41 may be learned.
In the estimation unit 36, a noise factor greatly affecting noise
is estimated from the operation information using the model learned
by the learning unit 35. For example, the operation information
corresponding to the noise factor may be estimated by the following
procedure.
Procedure c1) Targeted noise information is set to noise A.
Procedure c2) Operation information of a plurality of times is
randomly selected from the collected operation information.
Procedure c3) With regard to each selected operation information
item, a slope of an input value with respect to a value of noise A
is calculated using the learned model.
Procedure c4) An average value of slopes is calculated, and an
input signal having a particularly large slope is estimated as
operation information corresponding to a noise factor. However, in
a case in which a difference in slope is small when compared to
another input signal, the input signal is not estimated as a noise
factor.
FIGS. 3A-1 and 3A-2 and FIGS. 4B-1 and 4B-2 are diagrams
illustrating an image of a procedure of estimating the
above-described noise factor. In FIGS. 3A-1 and 3A-2 and FIGS. 4B-1
and 4B-2, for example, a noise value corresponding to an object of
estimation of a factor is set to A, w.sub.1 denotes a feed speed
value of operation information, w.sub.2 denotes a spindle speed
value of operation information, X.sub.1 denotes a correlation
coefficient of a feed speed and a noise value A(w), X.sub.2 denotes
a correlation coefficient of a spindle speed and a noise value
A(w), and superscript (n) denotes a serial number n assigned to a
plurality of operation information items selected in the
above-described procedure c2. The noise value A modeled by machine
learning is regarded as a function of the operation information w,
and thus is denoted by A(w) in the figure.
As illustrated in FIG. 3A-1, the model learned by the learning unit
35 is analyzed to calculate a correlation coefficient
X.sub.1.sup.(1) of a feed speed and a noise value A(w) for specific
operation information w.sup.(1). Similarly, as illustrated in FIG.
3A-2, the model learned by the learning unit 35 is analyzed to
calculate a correlation coefficient X.sub.1.sup.(2) of a feed speed
and a noise value A(w) for specific operation information
w.sup.(2). In this way, as illustrated in FIG. 4B-1, with regard to
the feed speed w.sub.1 in each of randomly extracted operation
information items w.sup.(1) to w.sup.(n), each of the correlation
coefficients X.sub.1.sup.(1) to X.sub.1.sup.(n) is obtained by
analyzing the model learned by the learning unit 35, and a value
obtained by averaging these correlation coefficients is calculated.
When an average value (0.5 in FIG. 4B-1) of the correlation
coefficients calculated in this way is larger than a predetermined
threshold value (for example, a threshold value 0.1) determined in
advance, a change of the operation information (the feed speed
w.sub.1 in FIG. 4B-1) greatly affects the noise value A (w), and
thus the feed speed w.sub.1 may be estimated as a noise factor of
the noise value A. In an example illustrated in FIG. 4B-2, since an
average value (0.025) of respective correlation coefficients
X.sub.1.sup.(1) to X.sub.1.sup.(n) of the spindle speed w.sub.2 is
a small value, a change of the spindle speed w.sub.2 does not
greatly affect the noise value A(w), and thus it may be estimated
that the spindle speed w.sub.2 is not a noise factor of the noise
value A.
In FIGS. 3A-1 and 3A-2 and FIGS. 4B-1 and 4B-2 described above, for
the sake of simplicity of description, each operation information
item is illustrated as a two-dimensional (2D) graph with respect to
a noise value, and then a slope value is obtained as a correlation
coefficient. However, in practice, as illustrated in FIG. 5, a
slope value for each direction at each point in a multi-dimensional
function corresponding to the number of inputs of operation
information is calculated as a correlation coefficient.
In the estimation unit 36, a machine having a low noise immunity
may be estimated using the model learned by the learning unit 35.
For example, the machine having the low noise immunity may be
estimated in the following procedure.
Procedure d1) One piece of data corresponding to an appropriate
time is selected from collected operation information.
Procedure d2) The selected operation information is input as an
input value to the model learned by the learning unit 35, and a
slope of an input value at which each output value increases is
calculated.
Procedure d3) Each input value is increased and decreased in a
direction of the calculated slope.
Procedure d4) Procedures d2 to d3 are repeated until one of outputs
exceeds a predetermined threshold value or converges.
Procedure d5) A manufacturing machine corresponding to an output
signal exceeding the threshold value in procedure d4 is estimated
to have a low noise immunity.
FIGS. 6C-1 and 6C-2 are diagrams illustrating an image of a
procedure of estimating the above-described low noise immunity
machine. In a case in which the noise value A(w) exceeds the
predetermined threshold value when the input value is increased in
procedure d3 as illustrated in FIG. 6C-1, it is possible to
estimate that noise may occur in the manufacturing machine during
operation, and the manufacturing machine may be estimated to have a
low noise immunity. In addition, in a case in which the noise value
A (w) does not exceed the predetermined threshold value and
converges (the noise value A(w) drops) when the input value is
increased in procedure d3 as illustrated in FIG. 6C-2, it is
possible to estimate that a noise value A corresponding to a level
that causes a problem during operation is not generated in the
manufacturing machine, and to estimate that there is no problem in
a noise immunity of the manufacturing machine.
The operation instruction change unit 37 instructs the machine
operation instruction unit 31 to change instruction content
transmitted to each manufacturing machine 41 based on a result of
estimating operation information corresponding to a noise factor by
the estimation unit 36 or a result of estimating a manufacturing
machine having a low noise immunity by the estimation unit. As an
example of changing an instruction transmitted to the machine
operation instruction unit 31 by the operation instruction change
unit 37, for example, at the time of operating a manufacturing
machine corresponding to a cause of operation information actually
estimated as a noise factor or a manufacturing machine estimated to
have a low noise immunity, a message that prompts a user to take
measures against noise related to operation information estimated
as a noise factor is displayed on a screen, etc. of an operation
panel of the manufacturing machine, or warns the user to change
operation information estimated as a noise factor to a value having
a level at which noise does not occur (for example, recommends
dropping a feed speed of the manufacturing machine). Further, the
operation instruction change unit 37 may report information related
to the operation information estimated as the noise factor or the
manufacturing machine estimated to have the low noise immunity to a
high-order server such as the production planning device 2.
The operation instruction change unit 37 may instruct the machine
operation instruction unit 31 to change operation instruction
content transmitted to each manufacturing machine 41 such that a
defect of the machine due to noise does not occur based on a result
of estimating operation information corresponding to a noise factor
by the estimation unit 36 or a result of estimating a manufacturing
machine having a low noise immunity by the estimation unit. As an
example of change of the operation instruction content of the
manufacturing machine, a used program is changed such that an
operation is performed using a method that does not cause such an
operation state with regard to a manufacturing machine
corresponding to a cause of the operation information estimated as
the noise factor, or an order is changed such that while the
manufacturing machine estimated to have the low noise immunity
operates, a manufacturing machine corresponding to a noise factor
with respect to the manufacturing machine is not instructed to
operate. Besides, it is possible to consider making a change such
that the operation information estimated as the noise factor is
decreased to a value corresponding to a level at which noise does
not occur (for example, the operation instruction is automatically
changed to drop a feed speed of the manufacturing machine).
The manufacturing cell 4 includes the at least one manufacturing
machine 41. In addition, each manufacturing machine 41 includes the
noise detector 42 and an operation information transmitter 43.
The noise detector 42 detects occurring noise using a sensor (not
illustrated), etc. installed in each part of the manufacturing
machine 41, and transmits a value of the detected noise to the cell
controller 3. In addition, the operation information transmitter 43
transmits information, which is collected by a controller of the
manufacturing machine 41 (not illustrated) from each part of the
manufacturing machine 41, to the cell controller 3 as operation
information.
Even though the embodiment of the invention has been described
above, the invention is not limited to the example of the
embodiment described above, and may be implemented in various
manners by making appropriate modifications.
* * * * *